The basic idea of this method is to use the capability of original GANs to transform random noise vectors into data that looks similar to data of a given training set. While the pix2pix network that was described earlier in this work performs a transformation between different image domains, more traditional GANs are designed to generate completely new data. This property was used for data augmentation tasks in the past, not only in the image domain [1]}, but also for audio classification tasks [2]}.
However, instead of generating new image data of carbon fiber defect images, our approach uses such a rather traditional GAN to create new label segmentation masks, which then can be transformed to defect image data by feeding it to our pix2pix network, as will be described in the next section. The benefit of generating label masks instead of real image data is that, on the one hand, the pixelwise structure of those label masks appears less complex due to its binary nature. On the other hand, the cascade of generating label masks in one step and transforming those label masks further to actual image data in another step makes those image/label pairs available for direct processing: they finally serve as a training set for segmentation networks.
